• The Journal of Korean Philosophical History
• /
• no.54
• /
• pp.273-300
• /
• 2017

This paper examines the characteristics of the traditional education contents that they have preserved and succeeded based on the recognition of 'Dongdo' by the modern intellectuals, and selects the contemporary contents of traditional education which are worthy of modern succession, It is aimed to categorize by classification and to reconstruct the contents accordingly. For this purpose, I will try to diagnose the problems of modern education and explore the possibilities of traditional ideas represented by Confucianism as a solution to solve and solve these problems. In particular, I will examine the positive and negative perceptions of intellectuals about studying abroad since the modern era, and examine what are the meaningful things to avoid and how to reconstruct the contents of traditional education in a modern way. Through this review, I will establish the principles for the modern reconstruction of the contents of the traditional education and finally discuss the parts that should be emphasized in the modern reconstruction of the contents of the traditional education and the composition of the alternative contents.

• The Korean Journal of Nuclear Medicine Technology
• /
• v.25 no.1
• /
• pp.41-43
• /
• 2021

Purpose In nuclear medicine blood tests, hemolysis samples are considered as inappropriate sample and are recommended not to be used for blood test. So, the lab are required to collect the blood again in the blood collection room However, The effect of hemolyzed samples on radioimmunoassay has not studied yet. This study was designed to evaluate effects of hemolysis on radioimmunoassay. Materials and Methods The kit manuals of 23 test items were reviewed to confirm whether hemolyzed samples were used. The subjects were 19 general applicants(male : 9, female : 13) and the samples were collected by each two SST tubes, one tube was obtained by centrifugation normally, and the other was obtained hemolyzed sample by centrifugation after external shock. It has been known that highly hemolyzed samples can affect the test results, so the test was performed using the severe hemolyzed sample. The test was performed for each test item using 23 normal serum and hemolysis serum, and SPSS19 program was used for statistical comparison of the test result. Results There was no significant difference between normal serum and hemolysis serum in 21 of 23 test items, but the results of insulin and C-peptide were significantly different(P<0.05). Conclusion It has been known that hemolysis in blood samples can affect the results of biochemical and hematological test, However, hemolysis effect is relatively low. Similarly, this study showed that hemolysis had not much effect on most of immunological radioimmunoassay except for some tests. Therefore, it is thought that the demand for re-collection due to hemolysis will be reduced in the laboratory, which will improve the work process of the laboratory.

• Journal of Intelligence and Information Systems
• /
• v.29 no.2
• /
• pp.85-100
• /
• 2023

Corporate ESG performance (environmental, social, and corporate governance) reflecting a company's strategic sustainability has emerged as one of the main factors in today's investment decisions. The traditional ESG performance rating process is largely performed in a qualitative and subjective manner based on the institution-specific criteria, entailing limitations in reliability, predictability, and timeliness when making investment decisions. This study attempted to predict the corporate ESG rating through automated machine learning based on quantitative and disclosed corporate financial information. Using 12 types (21,360 cases) of market-disclosed financial information and 1,780 ESG measures available through the Korea Institute of Corporate Governance and Sustainability during 2019 to 2021, we suggested a deep neural network prediction model. Our model yielded about 86% of accurate classification performance in predicting ESG rating, showing better performance than other comparative models. This study contributed the literature in a way that the model achieved relatively accurate ESG rating predictions through an automated process using quantitative and publicly available corporate financial information. In terms of practical implications, the general investors can benefit from the prediction accuracy and time efficiency of our proposed model with nominal cost. In addition, this study can be expanded by accumulating more Korean and international data and by developing a more robust and complex model in the future.

• 한국노년학
• /
• v.30 no.3
• /
• pp.1005-1020
• /
• 2010

This study was conducted to explore and understand the meaning of stigma of families having old people admitted to nursing home within the Korean culture. Data collection was performed through in-depth interviews and participant observations which were recorded and transcribed verbatim with the consent of the participants. The key informants were 12 people having the aged family member in nursing home. The data was collected from October 2008 to February 2009 until completed. Data were analyzed utilizing the taxonomic analysis method developed by Spradley. As a result, 24 themes, 8 categories and 4 cultural domains are founded from the cases. The cultural domains resulted from the analysis are: 『Incompetence of Oneself: 'Adaptation to Inevitable Realities', 'Difficulty of Economic Independence', 'Difficulty of the Subjective Self-assertion'』, 『Contradictoriness of Decision Making: 'Decision Making Different from Own Mind', 'Conflicts between Neighboring'』, 『Self-rationalization of Decision Making: 'Self-comfort of Decision Making'』, 『Shifting Responsibility: 'Services Different from that of Family', 'Laking in Sincerity of Responsible Institution'』. Theoretical model about stigma of the family having old people admitted to nursing home by the research result in the above was able to be confirmed that it was expressed with the original form of thought of recursive system which continuously showing the inconsistency of decision making, rationalizing decision making, and shifting one's own responsibility in the process of accomplishing the duty of supporting old people. Based on the results, I discussed the meaning of stigma of families having old people admitted to nursing home and provided recommendations for future research.

• Korean Journal of Social Welfare Studies
• /
• v.41 no.4
• /
• pp.5-44
• /
• 2010

The main purpose of this study is to critically examine the existing theoretical as well as analytical scope of 'sexual minority' in Social Welfare, and to (re)construct and expand it incorporating feminist theories on sexuality. The body of literature on sexual minority or homosexuality in social welfare in Korea as well as in the West can be characterized as two distinct features: first, medical discourse leaning on pathological perspective which perceives homosexuality as a disease or defect, homosexual as a pervert; and second, human rights perspective premised upon the idea of diversity and multi-culturalism, both which are anchoring at 'essentialism.' Based upon the understanding of sexuality as a social construct, we argue that feminist insight on sexuality can lead to reconceptualizing homosexuality and reorienting theories and practices in social welfare. From radical feminism to postmodern queer theories, feminists have developed diverse ideas and complex theories on sexuality and homosexuality, including the concept of 'compulsory heterosexuality,' 'lesbianism as political resistance,' and 'performative gender.' For feminists, particularly, sexuality which is constructed in the complex power matrix of dominations to producing and maintaining inequalities and discriminations is not merely a distinctive variable, but one of the important organizational principles such as gender, class, race, age, and nationality. This epistemological principle will hopefully shed lights on alternative 'knowledge' on homosexuality in social welfare, and lead to significant contribution to its critical expansion in theory and practice.

• Journal of Intelligence and Information Systems
• /
• v.26 no.4
• /
• pp.173-198
• /
• 2020

For a long time, many studies have been conducted on predicting the success of campaigns for customers in academia, and prediction models applying various techniques are still being studied. Recently, as campaign channels have been expanded in various ways due to the rapid revitalization of online, various types of campaigns are being carried out by companies at a level that cannot be compared to the past. However, customers tend to perceive it as spam as the fatigue of campaigns due to duplicate exposure increases. Also, from a corporate standpoint, there is a problem that the effectiveness of the campaign itself is decreasing, such as increasing the cost of investing in the campaign, which leads to the low actual campaign success rate. Accordingly, various studies are ongoing to improve the effectiveness of the campaign in practice. This campaign system has the ultimate purpose to increase the success rate of various campaigns by collecting and analyzing various data related to customers and using them for campaigns. In particular, recent attempts to make various predictions related to the response of campaigns using machine learning have been made. It is very important to select appropriate features due to the various features of campaign data. If all of the input data are used in the process of classifying a large amount of data, it takes a lot of learning time as the classification class expands, so the minimum input data set must be extracted and used from the entire data. In addition, when a trained model is generated by using too many features, prediction accuracy may be degraded due to overfitting or correlation between features. Therefore, in order to improve accuracy, a feature selection technique that removes features close to noise should be applied, and feature selection is a necessary process in order to analyze a high-dimensional data set. Among the greedy algorithms, SFS (Sequential Forward Selection), SBS (Sequential Backward Selection), SFFS (Sequential Floating Forward Selection), etc. are widely used as traditional feature selection techniques. It is also true that if there are many risks and many features, there is a limitation in that the performance for classification prediction is poor and it takes a lot of learning time. Therefore, in this study, we propose an improved feature selection algorithm to enhance the effectiveness of the existing campaign. The purpose of this study is to improve the existing SFFS sequential method in the process of searching for feature subsets that are the basis for improving machine learning model performance using statistical characteristics of the data to be processed in the campaign system. Through this, features that have a lot of influence on performance are first derived, features that have a negative effect are removed, and then the sequential method is applied to increase the efficiency for search performance and to apply an improved algorithm to enable generalized prediction. Through this, it was confirmed that the proposed model showed better search and prediction performance than the traditional greed algorithm. Compared with the original data set, greed algorithm, genetic algorithm (GA), and recursive feature elimination (RFE), the campaign success prediction was higher. In addition, when performing campaign success prediction, the improved feature selection algorithm was found to be helpful in analyzing and interpreting the prediction results by providing the importance of the derived features. This is important features such as age, customer rating, and sales, which were previously known statistically. Unlike the previous campaign planners, features such as the combined product name, average 3-month data consumption rate, and the last 3-month wireless data usage were unexpectedly selected as important features for the campaign response, which they rarely used to select campaign targets. It was confirmed that base attributes can also be very important features depending on the type of campaign. Through this, it is possible to analyze and understand the important characteristics of each campaign type.

• Journal of Intelligence and Information Systems
• /
• v.22 no.1
• /
• pp.187-204
• /
• 2016

Document classification based on emotional polarity has become a welcomed emerging task owing to the great explosion of data on the Web. In the big data age, there are too many information sources to refer to when making decisions. For example, when considering travel to a city, a person may search reviews from a search engine such as Google or social networking services (SNSs) such as blogs, Twitter, and Facebook. The emotional polarity of positive and negative reviews helps a user decide on whether or not to make a trip. Sentiment analysis of customer reviews has become an important research topic as datamining technology is widely accepted for text mining of the Web. Sentiment analysis has been used to classify documents through machine learning techniques, such as the decision tree, neural networks, and support vector machines (SVMs). is used to determine the attitude, position, and sensibility of people who write articles about various topics that are published on the Web. Regardless of the polarity of customer reviews, emotional reviews are very helpful materials for analyzing the opinions of customers through their reviews. Sentiment analysis helps with understanding what customers really want instantly through the help of automated text mining techniques. Sensitivity analysis utilizes text mining techniques on text on the Web to extract subjective information in the text for text analysis. Sensitivity analysis is utilized to determine the attitudes or positions of the person who wrote the article and presented their opinion about a particular topic. In this study, we developed a model that selects a hot topic from user posts at China's online stock forum by using the k-means algorithm and self-organizing map (SOM). In addition, we developed a detecting model to predict a hot topic by using machine learning techniques such as logit, the decision tree, and SVM. We employed sensitivity analysis to develop our model for the selection and detection of hot topics from China's online stock forum. The sensitivity analysis calculates a sentimental value from a document based on contrast and classification according to the polarity sentimental dictionary (positive or negative). The online stock forum was an attractive site because of its information about stock investment. Users post numerous texts about stock movement by analyzing the market according to government policy announcements, market reports, reports from research institutes on the economy, and even rumors. We divided the online forum's topics into 21 categories to utilize sentiment analysis. One hundred forty-four topics were selected among 21 categories at online forums about stock. The posts were crawled to build a positive and negative text database. We ultimately obtained 21,141 posts on 88 topics by preprocessing the text from March 2013 to February 2015. The interest index was defined to select the hot topics, and the k-means algorithm and SOM presented equivalent results with this data. We developed a decision tree model to detect hot topics with three algorithms: CHAID, CART, and C4.5. The results of CHAID were subpar compared to the others. We also employed SVM to detect the hot topics from negative data. The SVM models were trained with the radial basis function (RBF) kernel function by a grid search to detect the hot topics. The detection of hot topics by using sentiment analysis provides the latest trends and hot topics in the stock forum for investors so that they no longer need to search the vast amounts of information on the Web. Our proposed model is also helpful to rapidly determine customers' signals or attitudes towards government policy and firms' products and services.

• Journal of Korean Society of Transportation
• /
• v.20 no.2
• /
• pp.105-115
• /
• 2002

For many rears, traffic accident statistics are the most direct measure of safety for a signalized intersection. However it takes more than 2 or 3 yearn to collect certain accident data for adequate sample sizes. And the accident data itself is unreliable because of the difference between accident data recorded and accident that is actually occurred. Therefore, it is rather difficult to evaluate safety for a intersection by using accident data. For these reasons, traffic conflict technique(TCT) was developed as a buick and accurate counter-measure of safety for a intersection. However, the collected conflict data is not always reliable because there is absence of clear criteria for conflict. This study developed objective and accurate conflict criteria, which is shown below based on traffic engineering theory. Frist, the rear-end conflict is regarded, when the following vehicle takes evasive maneuver against the first vehicle within a certain distance, according to car-following theory. Second, lane-change conflict is regarded when the following vehicle takes evasive maneuver against first vehicle which is changing its lane within the minimum stopping distance of the following vehicle. Third, cross and opposing-left turn conflicts are regarded when the vehicle which receives green sign takes evasive maneuver against the vehicle which lost its right-of-way crossing a intersection. As a result of correlation analysis between conflict and accident, it is verified that the suggested conflict criteria in this study ave applicable. And it is proven that estimating safety evaluation for a intersection with conflict data is possible, according to the regression analysis preformed between accident and conflict, EPDO accident and conflict. Adopting the conflict criteria suggested in this study would be both quick and accurate method for diagnosing safety and operational deficiencies and for evaluation improvements at intersections. Further research is required to refine the suggested conflict criteria to extend its application. In addition, it is necessary to develope other types of conflict criteria, not included in this study, in later study.

• Korean Journal of Environmental Biology
• /
• v.19 no.4
• /
• pp.261-269
• /
• 2001

The purpose of present study was to introduce a multimetric approach, so called the Index of Biological Integrity (IBI) as a tool for evaluations of water environments. We used 11 metric systems for the IBI to evaluate stream conditions, based on the fish community, and modified 5 original metric attributes suggested by Karr (1981). Overall IBI values in Kap Stream averaged 36 (n = 5) and ranged 17${\sim}$49, indicating a 'fair condition' according to the modified criteria of Karr (1981) and U.S. EPA (1993). However, there were distinct differences in the IBI values among 5 study sites. The IBI values at sites 1, 2, and 3 were 49, 45, and 41, which indicated 'good${\sim}$excellent', 'good', and 'fair' condition, respectively, while values at sites 4 and 5 were 17 and 29, which indicated 'very poor' and 'poor', respectively. The minimum IBI at site 4 was probably due to continuous inputs of wastewater from wastewater disposal plants. The condition at site 4 resulted in predominance of tolerant species (50%), omnivore species (50%), and high abnormalies (43%). In the mean time, the IBI value at site 5, located near 5km downstream from the site 4, increased compared to that of site 4, and this seemed to be a result of recovery of water quality as the polluted water goes downward. We believe that present bioassessment methodology of IBI applied in this study may be used as a key tool to set up specific goals for stream restoration plans and dentify recovery levels of lotic ecosystems after restoration activities(i.e., prevention of point-source pollutant input, restoration of physical habitats, construction of riparian vegetation) as well as a biological measure diagnosing current stream conditions.

• The Journal of the Korea Contents Association
• /
• v.6 no.4
• /
• pp.146-157
• /
• 2006

Causal relationship means what relations the result occurred have with a fact as a reason. In general, a formular that no result exists without reasons is used for the method to confirm existence and inexistence of causal relationship. Problematic causal relationships in Private Law are reparations (Article No. 393 of Private Law) due to debt nonfulfillment and reparation due to tort (Application of Article No. 393 by Article No. 750, and No. 763 of Private Law). The purpose pursued by reparation system in private law is to promote equal burden of damages, and the range of reparation at this time is decided by the range of damage and the range of damage is decided by the principle of causal relationship. That the causal relationship theory fairly causes confusion by treating one problem and the other problem as the same thing, instead of dividing them according to the purpose of protection presented by the law is a reason of the criticism from different views.